Automatic Extraction Method Research Articles

Automated extraction method of STM for 3D topology optimization based on moving morphable components

The Strut-and-Tie Method is considered a helpful tool for the analysis and design of reinforced concrete structures and has been incorporated into different structural design codes. However, the low efficiency of the construction of the three-dimensional (3D) Strut-and-Tie model (STM) has been a key problem limiting the widespread use of the STM. To realize the efficient construction of STM, an automated extraction method based on the moving morphable components (MMC) is established. The presented method is divided into four parts: 3D topology optimization, curve skeleton extraction, spatial frame extraction, and shape optimization. To improve the computational efficiency, the 3D topology optimization and curve skeleton extraction are based on the MMC method and the Laplacian method based on the mean curvature flow (MCF), respectively. A spatial frame consistent with the topological form of the optimization result is formed by point identification and connection. In shape optimization, the truss-like index that can reflect the proportion of shear forces in the internal forces of the bars is introduced. The results of numerical examples show that the method efficiently constructs the STM with reasonable stress and regular geometry. A smooth and medial curve skeleton is extracted from the optimized structure by the Laplacian extraction method. Shape optimization constrained by the truss-like index enhances the regularity of STM and realizes the change of optimized structures from frames to trusses.

Can storage-discharge characteristics of karst matrix system quantified through recession analysis be reliable?

Storage and subsequent release of water in matrix system is a key function of karst catchments that controlling baseflow variation. Hydrograph recession analysis is currently the economic way to assess storage-discharge characteristics broadly at the catchment scale. However, there is large uncertainty in the related quantification due to recession data extraction. This study, by combining recursive digital filters with different automatic extraction methods, slow flow recession segments were extracted for hydrograph recession analysis and the further dynamic matrix storage (DMS) and related recession time (RT) assessment. The diversity and consistence among DMS and RT derived from different recession extraction methods (REMs) were analyzed, using hydrometric data in 20 catchments in the Wujiang river Basin in southwest China. Results indicate that the estimates of DMS and RT remarkedly varied between different REMs, however the order in which they ranked was mostly consistent. Moreover, the relationships between the derived DMS and RT with catchment physical features that potentially control storage and release processes are mostly consistent. Larger DMS are strongly associated with catchments featured as lower soil saturated hydraulic conductivity and smoother hydrographs. Longer RT are mostly related with drier catchments characterized as less variation of elevation and lower soil saturated hydraulic conductivity. This study highlights not only the uncertainty in quantifying storage and accompanied release characteristics, but also the reliability of storage-discharge characteristics quantified through recession analysis in terms of catchment comparison. Considering the diversity among catchments, ensemble of multi-method estimates of DMS and RT can enhance our understanding of the storage-discharge processes in the karst matrix system.

Trading Community Analysis of Countries' Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data.

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper's findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies-Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe-Asia), another community handling 23% (serving Asia-Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America).

Relationship between polymorphism of GDF5 and CAPN1 genes in kalmyk breed cattle with economic and biological traits

The article presents the results of polymorphism of the GDF5 and CAPN1 genes in cattle of the Kalmyk breed, owned by the agricultural production company “Plodovitoe” of the Maloderbetovsky district of the Republic of Kalmykia, in the amount of 60 heads. DNA samples were isolated from whole blood taken from the jugular vein by an automatic extraction method. A real-time polymerase chain reaction was carried out to determine the polymorphism T586C of the GDF5 gene and C316G of the CAPN1 gene. In the studied herd of cattle, animals with the TT genotype predominate - 68.3 % of the population, the number of animals with the TC and CC genotypes is 21.7 % and 10 %. When studying the polymorphism of the CAPN1 gene, it was revealed that 23.3 % of the bulls of the studied population of Kalmyk cattle have the desired genotype CC, while the heterozygous genotype is present in 16.7 %, and the recessive genotype GG is present in 60 % of animals. The frequency of occurrence of the T allele of the GDF5 gene is 0.792, which exceeds the indicator of the C allele (0.208) by 58.2 %, the observed frequency of TT and CC genotypes exceeds the expected indicator by 5.6 and 5.7 %, respectively, while the indicator of the TC genotype is lower than the expected frequency by 11.2 %. The frequency of occurrence of the G allele (0.683) is 36.6 % higher than that of the C allele (0.317) of the CAPN1 gene. It is noted that the GG genotype with an index of 0.600 occurs with the highest frequency, while the CG genotype has a frequency of 0.167 (43.3 % less), the CC genotype – 0.233 (36.7 % less). The indicators of the observed frequency of the CC and GG genotypes are 13.3% higher than expected, the heterozygous genotype has an excess of the expected frequency by 26.6 %. Bulls with the TT genotype of the GDF5 gene outperformed their peers with the TC and CC genotypes in all measurements. When studying the live weight of Kalmyk cattle at the age of 15 months, a positive effect of the CC genotype of the CAPN1 gene was revealed. Thus, the group of animals with this genotype had the largest live weight and surpassed their peers with the genotypes CG (by 9.9 %) and GG (by 11.1 %).

Optimizing Prostate Cancer Radiotherapy: Advanced Machine Learning in Virtual Patient-Specific Plan Verification-Review Study

Background: Virtual patient-specific plan verification in radiotherapy is critical to ensure precise dose delivery while minimizing healthy tissue exposure, particularly in prostate cancer treatments. Prior studies, however, have overlooked the physical implications of predictor features and lacked comprehensive decision support tools, leading to gaps in understanding and practical application. This research aims to bridge these gaps by providing a nuanced understanding of predictor features, exploring advanced automatic feature extraction methods, emphasizing model reliability, and proposing a comprehensive decision support tool. Our objective is to optimize radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy. Methods: This research employs a literature review approach. An extensive literature study served as the foundation. Results: Our study reveals that understanding the physical implications of predictor features significantly enhances prediction accuracy. Utilizing Convolutional Neural Networks (CNN) models for automatic feature extraction improves prediction performance, providing robust and transferable results. Conclusions: By emphasizing model reliability through the integration of treatment plan parameters, our approach ensures stable predictions across diverse patient cases. The proposed decision support tool offers clinicians detailed insights into predicted dose deliverability, facilitating informed decision-making for patient-specific treatment plans. Through these advancements, our research contributes to the optimization of radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy.

The diachronic change of English relativizers: a case study in the State of the Union addresses across two centuries

Abstract Different types of relativizers are used to introduce relative clauses, and the change in the use of relativizers has attracted attention in recent decades. Despite progress in this area, the challenge of extracting relative clauses, especially those with zero relativizers, has limited our understanding of relativizer change. To this end, we investigated the evolving patterns of relativizers in the State of the Union addresses spanning two centuries, employing novel methodologies developed for this purpose. Different from findings of previous studies, our results showed an increase of that and a decrease of which in subject relative clauses, and an increase of zero relativizers in object relative clauses. The change could be attributed to changes in factors concerning text complexity and styles. The faster change rate around 1940s can be explained by factors such as prescriptivism and the transition in speech styles. Methodologically, our study has confirmed dependency parsing as a reliable method for automatic extraction of relative clauses.

Automatic extraction of geological discontinuities of a tunnel surface by integrating multiple features

In water conservancy, transportation, and mining projects, the timely acquisition of geological structural information from tunnels is critical in the analysis of engineering geological problems during the investigation and construction stages. The acquisition of comprehensive and accurate geological information from a tunnel surface remains challenging. This study provides an automatic extraction method for geological discontinuities on a tunnel surface by integrating 2D textural semantic features and 3D geological semantic features. A dense point cloud is generated using multiline parallel sequence images, after which the 3D geological semantic features, including the local geological attitude, are calculated. Through a virtual projection from 3D to 2D, the red, green, and blue (RGB) images and geological semantic images based on views of the interior umbrella arch and the sidewalls of the tunnel surface are obtained. The feature mapping between the 2D textural semantic features and the 3D geological semantic features is determined accordingly. The virtual RGB images and geological semantic images serve as dual inputs for ensemble learning for pixel block segmentation, and the output is a similarity probability tensor that describes the probability that each pixel will belong to its surrounding pixel blocks. The pixel blocks are clustered on the basis of pole and contour plots of their geological attitudes to extract geological discontinuities. Experiments were conducted to confirm and evaluate the feasibility and veracity of the proposed method. The developed method automatically extracts geological discontinuities of a tunnel surface and extends the scope of surveying and mapping through geological remote sensing.

Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection

Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation of flood inundation extent during the rainy period. Post-classification comparison is one of the fundamental methods for flood mapping of multi-source imagery, as the image characteristics of SAR and optical imagery are inherently different. However, the accuracy of flood mapping depends on the accuracy of water body delineation from single temporal imagery, which can lead to error propagation in flood extent determination. Change detection methods can extract flood extent directly from pre- and post-event imagery, but the models need to learn a consistent flood feature description between optical and SAR imagery from a limited flood dataset. Here, we propose an automatic method for flood inundation extent extraction using pre- and post-event multi-source imagery that requires only a small number of pseudo-change labels. The methodology adopts a pseudo-Siamese network framework as a Heterogeneous - Flood Inundation Extraction Network (H-FIENet), to detect flood extent between the pre‐ and post-event heterogeneous images. Spatially inconsistent multi-source pre- and post-event imagery was used to create a pseudo flood dataset, and this dataset was used to transfer water body feature knowledge from the pre-trained model to the flood extraction model using a cross-task knowledge transfer strategy. Pre- and post-event images from different satellite sources and different flood scenarios were used to evaluate the performance of H-FIENet. We found that: (1) The methodology produced an overall accuracy of over 0.94 for flood inundation extraction from multi-source heterogeneous pre- and post-event imagery. (2) H-FIENet can detect both expansion and recession of the flood extent using any dual-temporal imagery. Our work makes it possible to automate time-series flood monitoring from multi-source remote sensing imagery.

An automatic shoreline extraction method from SAR imagery using DeepLab-v3+ and its versatility

ABSTRACT This study presented an automatic shoreline extraction method from synthetic aperture radar (SAR) imagery using a semantic image segmentation model, DeepLab-v3+. Shorelines extracted from optical satellite images were employed as ground truth. More than 130,000 pairs of labeled images were generated, which substantially enhances the dataset’s objectivity for model training. By using different combination of SAR images from beaches with varying characteristics, several models were constructed. These models were then applied to 15 beaches of Japan to validate their accuracy and versatility. The developed machine learning model demonstrates high accuracy in shoreline extraction, particularly when trained with a diverse set of images from various beaches. This versatility is crucial for the model’s applicability across different coastal environments, underscoring the importance of incorporating a wide range of beach properties into the training dataset.

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

Computer vision-based model for detecting turning lane features on Florida’s public roadways from aerial images

ABSTRACT Efficient collection of roadway geometry data is crucial for effective transportation planning, maintenance, and design. Current methods involve land-based techniques like field inventory and aerial-based methods such as satellite imagery. However, land-based approaches are labor-intensive and costly, prompting the need for more efficient methodologies. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. This study proposes a computer vision-based approach to detect turning lane markings from aerial images in Florida. The method aims to identify aged or faded markings, compare lane locations with other features, and analyze intersection crashes. Validation in Leon County achieved 80.4% accuracy, detecting over 13,800 turning lane features in Duval County, Florida. This data integration offers valuable insights for policymakers and road users, highlighting the significance of automated extraction methods in transportation planning and safety.

Полиморфизм гена GDF5 крупного рогатого скота калмыцкой породы и его влияние на экстерьерные показатели

Purpose. Study of the influence of polymorphism of GDF5 gene of Kalmyk cattle on exterior characteristics. Materials and Methods. The polymorphism of the GDF5 gene was examined in 60 individuals of Kalmyk cattle breed belonging to the APC “Plodovitoe” of the Maloderbetovsky district of the Republic of Kalmykia. DNA samples were isolated from whole blood collected from the jugular vein using an automated extraction method. Real-time polymerase chain reaction was performed to determine the T586C polymorphism of the GDF5 gene. Results. Among the studied herd of cattle, individuals with the TT genotype predominated - they accounted for 68.3% of the total. Animals with TC and CC genotypes accounted for 21.7 and 10.0%, respectively. The frequency of occurrence of the T allele of GDF5 gene was 0.792, which exceeded the frequency of the C allele (0.208) by 58.2%. The observed frequency of the TT and CC genotypes exceeded the expected ones by 5.6 and 5.7%, respectively. At the same time, the frequency of the TC genotype was lower than expected by 11.2%. Individuals with the TT genotype of the GDF5 gene were superior to their peers with the TC and CC genotypes in all measured parameters. Conclusion. TT genotype of GDF5 gene in the studied livestock of Kalmyk cattle breed, owned by the APC “Plodovitoye”, occurs with the highest frequency (0.683). A positive effect of the TT genotype of GDF5 gene on linear physique measurements of Kalmyk cattle was revealed.

Extraction of block walls from point clouds measured by Mobile Mapping System

Abstract. To solve the problem of collapsing block walls widely used in Japan, this study proposes a method for extracting block walls using 3D point cloud data measured by the Mobile Mapping System (MMS). Unlike conventional methods, this method identifies block walls based on geometric features without relying on MMS trajectory data or deep learning inference results. In addition, the computational load is low and manual correction can be minimized. In our experiments, we used point cloud data collected in urban areas in Japan and achieved a precision of 0.750, recall of 0.810, and F-measure of 0.779. The results demonstrate the effectiveness of this method for automatic extraction of block walls and rapid assessment of collapse risk and are expected to contribute to safety measures in areas with high seismic risk.

A data integration framework of additive manufacturing based on FAIR principles

Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration.Graphical abstract

Automatic Detection and Analysis of the Shoreline Change Rate at Maravanthe Coast, India

Maravanthe Beach has been witnessing erosion due to waves caused by tidal forces, destroying vegetation, and causing enormous damage to people’s lives, livestock, and property. Hence, an attempt has been made to understand the shoreline change analysis using the automatic shoreline extraction method and determine the rate of change in terms of erosion and accretion rate before and after the construction of coastal structures during the period from 2014 to 2023. The SWAT model was applied to analyze the influence of surface runoff on shoreline changes near Maravanthe Beach for the periods 2004–2014 and 2012–2023. Also, runoff was chosen as one of the parameters for validation of the analysis. It was noticed that the shoreline stretch had experienced significant erosion prior to the construction of shore protection infrastructure. Soon after the deployment of certain shore structures, the shoreline progressed from a high erosion stretch to a low erosion stretch. This study reveals that Maravanthe Beach witnessed greater erosion during the period 2014–2018 than it did from 2019–2023 and also observed a decrease in erosion from 11% to 3% and an increase in the accretion zones from 2 to 9%.

The Limits of Words: Expanding a Word-Based Emotion Analysis System with Multiple Emotion Dictionaries and the Automatic Extraction of Emotive Expressions

Wide adoption of social media has caused an explosion of information stored online, with the majority of that information containing subjective, opinionated, and emotional content produced daily by users. The field of emotion analysis has helped effectively process such human emotional expressions expressed in daily social media posts. Unfortunately, one of the greatest limitations of popular word-based emotion analysis systems has been the limited emotion vocabulary. This paper presents an attempt to extensively expand one such word-based emotion analysis system by integrating multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. We first leverage diverse emotive expression dictionaries to expand the emotion lexicon of the system. To do that, we solve numerous problems with the integration of various dictionaries collected using different standards. We demonstrate the performance improvement of the system with improved accuracy and granularity of emotion classification. Furthermore, our automatic extraction mechanism facilitates the identification of novel emotive expressions in an emotion dataset, thereby enriching the depth and breadth of emotion analysis capabilities. In particular, the automatic extraction method shows promising results for applicability in further expansion of the dictionary base in the future, thus advancing the field of emotion analysis and offering new avenues for research in sentiment analysis, affective computing, and human–computer interaction.

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography.

Automatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning. We include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments. The method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an score of 0.85. Evaluation of our combined method leads to an average score of 0.74. The results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.

Automatic Extraction of Standard Multimodal Knowledge for the Petroleum Field

As the development of network and digitalization is accelerated, huge amount of multi-modal data is generated in the standardization activities of petroleum industry exploration and development, and the number of standards is large, covering a wide range of domains and being specialized. Based on the inherent characteristics of multi-modal data and standard knowledge organization framework, this paper proposes a method for automatic knowledge extraction of standard information from standard data of petroleum industry exploration and development. This paper advances the theory depth of knowledge organization of multi-modal standard knowledge and provides effective support for standard knowledge ordination, precise knowledge service and deep knowledge discovery in petroleum industry.

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.